• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

Learning globally-consistent local distance functions for shape-based image retrieval and classification (2007)

Cached

  • Download as a PDF

Download Links

  • [www.cs.berkeley.edu]
  • [www-rcf.usc.edu]
  • [www-rcf.usc.edu]
  • [www-bcf.usc.edu]
  • [www-rcf.usc.edu]
  • [www.cs.berkeley.edu]
  • [www.eecs.berkeley.edu]
  • [www.eecs.berkeley.edu]
  • [www.cs.huji.ac.il]
  • [www.cs.huji.ac.il]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]
  • [vc.cs.nthu.edu.tw]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Andrea Frome , Fei Sha , Yoram Singer , Jitendra Malik
Venue:In ICCV
Citations:149 - 3 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@INPROCEEDINGS{Frome07learningglobally-consistent,
    author = {Andrea Frome and Fei Sha and Yoram Singer and Jitendra Malik},
    title = {Learning globally-consistent local distance functions for shape-based image retrieval and classification},
    booktitle = {In ICCV},
    year = {2007}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions— a different parameterized function for every image of our training set—whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of additional heuristics or training. Here we introduce a different approach that has the advantage that it learns distance functions that are globally consistent in that they can be directly compared for purposes of retrieval and classification. The output of the learning algorithm are weights assigned to the image features, which is intuitively appealing in the computer vision setting: some features are more salient than others, and which are more salient depends on the category, or image, being considered. We train and test using the Caltech 101 object recognition benchmark. Using fifteen training images per category, we achieved a mean recognition rate of 63.2 % and

Keyphrases

shape-based image retrieval    globally-consistent local distance function    local distance function    image-to-image distance function    distance function    single global distance function    object recognition work    visual category recognition    different approach    computer vision setting    patch-based feature vector    novel approach    additional heuristic    object recognition benchmark    mean recognition rate    following property    different category    large-margin formulation    test time    different parameterized function    fifteen training image    image feature    distance metric learning    learning algorithm   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University